Update

results.py

@@ -26,7 +26,7 @@ VERSION = datasets.Version("1.0.1")
 EVAL_LANGS = ['af', 'afr-eng', 'am', "amh", 'amh-eng', 'ang-eng', 'ar', 'ar-ar', 'ara-eng', 'arq-eng', 'arz-eng', 'ast-eng', 'awa-eng', 'az', 'aze-eng', 'bel-eng', 'ben-eng', 'ber-eng', 'bn', 'bos-eng', 'bre-eng', 'bul-eng', 'cat-eng', 'cbk-eng', 'ceb-eng', 'ces-eng', 'cha-eng', 'cmn-eng', 'cor-eng', 'csb-eng', 'cy', 'cym-eng', 'da', 'dan-eng', 'de', 'de-fr', 'de-pl', 'deu-eng', 'dsb-eng', 'dtp-eng', 'el', 'ell-eng', 'en', 'en-ar', 'en-de', 'en-en', 'en-tr', 'eng', 'epo-eng', 'es', 'es-en', 'es-es', 'es-it', 'est-eng', 'eus-eng', 'fa', 'fao-eng', 'fi', 'fin-eng', 'fr', 'fr-en', 'fr-pl', 'fra', 'fra-eng', 'fry-eng', 'gla-eng', 'gle-eng', 'glg-eng', 'gsw-eng', 'hau', 'he', 'heb-eng', 'hi', 'hin-eng', 'hrv-eng', 'hsb-eng', 'hu', 'hun-eng', 'hy', 'hye-eng', 'ibo', 'id', 'ido-eng', 'ile-eng', 'ina-eng', 'ind-eng', 'is', 'isl-eng', 'it', 'it-en', 'ita-eng', 'ja', 'jav-eng', 'jpn-eng', 'jv', 'ka', 'kab-eng', 'kat-eng', 'kaz-eng', 'khm-eng', 'km', 'kn', 'ko', 'ko-ko', 'kor-eng', 'kur-eng', 'kzj-eng', 'lat-eng', 'lfn-eng', 'lit-eng', 'lin', 'lug', 'lv', 'lvs-eng', 'mal-eng', 'mar-eng', 'max-eng', 'mhr-eng', 'mkd-eng', 'ml', 'mn', 'mon-eng', 'ms', 'my', 'nb', 'nds-eng', 'nl', 'nl-ende-en', 'nld-eng', 'nno-eng', 'nob-eng', 'nov-eng', 'oci-eng', 'orm', 'orv-eng', 'pam-eng', 'pcm', 'pes-eng', 'pl', 'pl-en', 'pms-eng', 'pol-eng', 'por-eng', 'pt', 'ro', 'ron-eng', 'ru', 'run', 'rus-eng', 'sl', 'slk-eng', 'slv-eng', 'spa-eng', 'sna', 'som', 'sq', 'sqi-eng', 'srp-eng', 'sv', 'sw', 'swa', 'swe-eng', 'swg-eng', 'swh-eng', 'ta', 'tam-eng', 'tat-eng', 'te', 'tel-eng', 'tgl-eng', 'th', 'tha-eng', 'tir', 'tl', 'tr', 'tuk-eng', 'tur-eng', 'tzl-eng', 'uig-eng', 'ukr-eng', 'ur', 'urd-eng', 'uzb-eng', 'vi', 'vie-eng', 'war-eng', 'wuu-eng', 'xho', 'xho-eng', 'yid-eng', 'yor', 'yue-eng', 'zh', 'zh-CN', 'zh-TW', 'zh-en', 'zsm-eng']
 
 # v_measures key is somehow present in voyage-2-law results and is a list
-SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures"]
+SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures", "scores_per_experiment"]
 
 # Use "train" split instead
 TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
@@ -36,6 +36,8 @@ VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "LEMBSummScreenFDRetrieval", "M
 DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"]
 # Use "test.full" split
 TESTFULL_SPLIT = ["OpusparcusPC"]
+# Use "standard" split
+STANDARD_SPLIT = ["BrightRetrieval"]
 
 TEST_AVG_SPLIT = {
     "LEMBNeedleRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
@@ -61,6 +63,7 @@ MODELS = [
     "LLM2Vec-Sheared-Llama-unsupervised",
     "LaBSE",
     "OpenSearch-text-hybrid",
+    "SFR-Embedding-Mistral",
     "all-MiniLM-L12-v2",
     "all-MiniLM-L6-v2",
     "all-mpnet-base-v2",
@@ -124,12 +127,14 @@ MODELS = [
     "google-gecko.text-embedding-preview-0409",
     "gottbert-base",
     "gte-Qwen1.5-7B-instruct",
+    "gte-Qwen2-7B-instruct",
     "gtr-t5-base",
     "gtr-t5-large",
     "gtr-t5-xl",
     "gtr-t5-xxl",
     "herbert-base-retrieval-v2",
     "instructor-base",
+    "instructor-large",
     "instructor-xl",
     "jina-embeddings-v2-base-en",
     "komninos",
@@ -228,8 +233,8 @@ def get_paths():
             if not os.path.isdir(os.path.join(results_model_dir, revision_folder)):
                 continue
             for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)):
-                if (res_file.endswith(".json")) and not(res_file.endswith("overall_results.json")):
-                    results_model_file = os.path.join(results_model_dir, res_file)
+                if (res_file.endswith(".json")) and not(res_file.endswith(("overall_results.json", "model_meta.json"))):
+                    results_model_file = os.path.join(results_model_dir, revision_folder, res_file)
                     files[model_dir].append(results_model_file)
     with open("paths.json", "w") as f:
         json.dump(files, f, indent=2)
@@ -265,9 +270,10 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         path_file = dl_manager.download_and_extract(URL)
+        # Local debugging:
+        #with open("/Users/muennighoff/Desktop/results/paths.json") as f:
         with open(path_file) as f:
             files = json.load(f)
-
         downloaded_files = dl_manager.download_and_extract(files[self.config.name])
         return [
             datasets.SplitGenerator(
@@ -298,6 +304,8 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                     split = "dev"
                 elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
                     split = "test.full"
+                elif (ds_name in STANDARD_SPLIT) and ("standard" in res_dict):
+                    split = "standard"
                 elif (ds_name in TEST_AVG_SPLIT):
                     # Average splits
                     res_dict["test_avg"] = {}
@@ -329,17 +337,40 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                 ### New MTEB format ###
                 if isinstance(res_dict, list):
                     for res in res_dict:
-                        lang = res.get("languages", [""])
-                        assert len(lang) == 1, "Only single-languages supported for now"
-                        lang = lang[0].replace("eng-Latn", "")
+                        lang = res.pop("languages", [""])
+                        subset = res.pop("hf_subset", "")
+                        if len(lang) == 1:
+                            lang = lang[0].replace("eng-Latn", "")
+                        else:
+                            lang = "_".join(lang)
+                        if not lang:
+                            lang = subset
                         for metric, score in res.items():
                             if metric in SKIP_KEYS: continue
-                            out.append({
-                                "mteb_dataset_name": ds_name,
-                                "eval_language": lang,
-                                "metric": metric,
-                                "score": score * 100,
-                            })
+                            if isinstance(score, dict):
+                                # Legacy format with e.g. {cosine: {spearman: ...}}
+                                # Now it is {cosine_spearman: ...}
+                                for k, v in score.items():
+                                    if not isinstance(v, float): 
+                                        print(f'WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}')
+                                        continue
+                                    if metric in SKIP_KEYS: continue
+                                    out.append({
+                                        "mteb_dataset_name": ds_name,
+                                        "eval_language": lang,
+                                        "metric": metric + "_" + k,
+                                        "score": v * 100,
+                                    })
+                            else:
+                                if not isinstance(score, float): 
+                                    print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric} {k}')
+                                    continue
+                                out.append({
+                                    "mteb_dataset_name": ds_name,
+                                    "eval_language": lang,
+                                    "metric": metric,
+                                    "score": score * 100,
+                                })
 
                 ### Old MTEB format ###
                 else:

results/Cohere-embed-english-v3.0/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
+{
+    "dataset_revision": "a75a0eb",
+    "mteb_version": "1.12.79",
+    "scores": {
+        "standard": [
+            {
+                "hf_subset": "psychology",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.21823,
+                "ndcg_at_1": 0.18812,
+                "ndcg_at_5": 0.20228,
+                "ndcg_at_10": 0.21823,
+                "ndcg_at_25": 0.24696,
+                "ndcg_at_50": 0.26808,
+                "ndcg_at_100": 0.29599,
+                "map_at_1": 0.05814,
+                "map_at_5": 0.12046,
+                "map_at_10": 0.14212,
+                "map_at_25": 0.16405,
+                "map_at_50": 0.16957,
+                "map_at_100": 0.17443,
+                "Recall_at_1": 0.05814,
+                "Recall_at_5": 0.16749,
+                "Recall_at_10": 0.25134,
+                "Recall_at_25": 0.3758,
+                "Recall_at_50": 0.4573,
+                "Recall_at_100": 0.57679,
+                "precision_at_1": 0.18812,
+                "precision_at_5": 0.13663,
+                "precision_at_10": 0.10198,
+                "precision_at_25": 0.06693,
+                "precision_at_50": 0.04436,
+                "precision_at_100": 0.02871,
+                "mrr": 0.27665
+            },
+            {
+                "hf_subset": "economics",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.20185,
+                "ndcg_at_1": 0.15534,
+                "ndcg_at_5": 0.19145,
+                "ndcg_at_10": 0.20185,
+                "ndcg_at_25": 0.22909,
+                "ndcg_at_50": 0.24993,
+                "ndcg_at_100": 0.26924,
+                "map_at_1": 0.03475,
+                "map_at_5": 0.10182,
+                "map_at_10": 0.11747,
+                "map_at_25": 0.13623,
+                "map_at_50": 0.14624,
+                "map_at_100": 0.15216,
+                "Recall_at_1": 0.03475,
+                "Recall_at_5": 0.17231,
+                "Recall_at_10": 0.22402,
+                "Recall_at_25": 0.34823,
+                "Recall_at_50": 0.44208,
+                "Recall_at_100": 0.51574,
+                "precision_at_1": 0.15534,
+                "precision_at_5": 0.12816,
+                "precision_at_10": 0.10388,
+                "precision_at_25": 0.07534,
+                "precision_at_50": 0.0532,
+                "precision_at_100": 0.03466,
+                "mrr": 0.25056
+            },
+            {
+                "hf_subset": "robotics",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.16211,
+                "ndcg_at_1": 0.18812,
+                "ndcg_at_5": 0.1599,
+                "ndcg_at_10": 0.16211,
+                "ndcg_at_25": 0.18741,
+                "ndcg_at_50": 0.21416,
+                "ndcg_at_100": 0.22839,
+                "map_at_1": 0.06417,
+                "map_at_5": 0.10481,
+                "map_at_10": 0.11268,
+                "map_at_25": 0.12302,
+                "map_at_50": 0.12867,
+                "map_at_100": 0.13067,
+                "Recall_at_1": 0.06417,
+                "Recall_at_5": 0.14288,
+                "Recall_at_10": 0.17651,
+                "Recall_at_25": 0.269,
+                "Recall_at_50": 0.3771,
+                "Recall_at_100": 0.43352,
+                "precision_at_1": 0.18812,
+                "precision_at_5": 0.09505,
+                "precision_at_10": 0.06535,
+                "precision_at_25": 0.04356,
+                "precision_at_50": 0.0295,
+                "precision_at_100": 0.01802,
+                "mrr": 0.24142
+            },
+            {
+                "hf_subset": "biology",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.18978,
+                "ndcg_at_1": 0.16505,
+                "ndcg_at_5": 0.17454,
+                "ndcg_at_10": 0.18978,
+                "ndcg_at_25": 0.22773,
+                "ndcg_at_50": 0.25247,
+                "ndcg_at_100": 0.28335,
+                "map_at_1": 0.0434,
+                "map_at_5": 0.10616,
+                "map_at_10": 0.12105,
+                "map_at_25": 0.1367,
+                "map_at_50": 0.14193,
+                "map_at_100": 0.14624,
+                "Recall_at_1": 0.0434,
+                "Recall_at_5": 0.18077,
+                "Recall_at_10": 0.23136,
+                "Recall_at_25": 0.33892,
+                "Recall_at_50": 0.42433,
+                "Recall_at_100": 0.55949,
+                "precision_at_1": 0.16505,
+                "precision_at_5": 0.12621,
+                "precision_at_10": 0.08641,
+                "precision_at_25": 0.05165,
+                "precision_at_50": 0.03282,
+                "precision_at_100": 0.02107,
+                "mrr": 0.27194
+            },
+            {
+                "hf_subset": "stackoverflow",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.16465,
+                "ndcg_at_1": 0.15385,
+                "ndcg_at_5": 0.14298,
+                "ndcg_at_10": 0.16465,
+                "ndcg_at_25": 0.1897,
+                "ndcg_at_50": 0.21659,
+                "ndcg_at_100": 0.24688,
+                "map_at_1": 0.03844,
+                "map_at_5": 0.08066,
+                "map_at_10": 0.09662,
+                "map_at_25": 0.10725,
+                "map_at_50": 0.11405,
+                "map_at_100": 0.1191,
+                "Recall_at_1": 0.03844,
+                "Recall_at_5": 0.14358,
+                "Recall_at_10": 0.21203,
+                "Recall_at_25": 0.28273,
+                "Recall_at_50": 0.36804,
+                "Recall_at_100": 0.48952,
+                "precision_at_1": 0.15385,
+                "precision_at_5": 0.09744,
+                "precision_at_10": 0.07778,
+                "precision_at_25": 0.05368,
+                "precision_at_50": 0.03966,
+                "precision_at_100": 0.02667,
+                "mrr": 0.24286
+            },
+            {
+                "hf_subset": "theoremqa_theorems",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.06038,
+                "ndcg_at_1": 0.03077,
+                "ndcg_at_5": 0.04492,
+                "ndcg_at_10": 0.06038,
+                "ndcg_at_25": 0.07583,
+                "ndcg_at_50": 0.09984,
+                "ndcg_at_100": 0.11176,
+                "map_at_1": 0.01282,
+                "map_at_5": 0.03846,
+                "map_at_10": 0.04515,
+                "map_at_25": 0.04835,
+                "map_at_50": 0.0522,
+                "map_at_100": 0.05341,
+                "Recall_at_1": 0.01282,
+                "Recall_at_5": 0.05385,
+                "Recall_at_10": 0.1,
+                "Recall_at_25": 0.15897,
+                "Recall_at_50": 0.27051,
+                "Recall_at_100": 0.33333,
+                "precision_at_1": 0.03077,
+                "precision_at_5": 0.02462,
+                "precision_at_10": 0.01692,
+                "precision_at_25": 0.01046,
+                "precision_at_50": 0.00862,
+                "precision_at_100": 0.00538,
+                "mrr": 0.06393
+            },
+            {
+                "hf_subset": "pony",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.01773,
+                "ndcg_at_1": 0.01786,
+                "ndcg_at_5": 0.01642,
+                "ndcg_at_10": 0.01773,
+                "ndcg_at_25": 0.01903,
+                "ndcg_at_50": 0.02745,
+                "ndcg_at_100": 0.03984,
+                "map_at_1": 0.0018,
+                "map_at_5": 0.00295,
+                "map_at_10": 0.00355,
+                "map_at_25": 0.00438,
+                "map_at_50": 0.00531,
+                "map_at_100": 0.00614,
+                "Recall_at_1": 0.0018,
+                "Recall_at_5": 0.00494,
+                "Recall_at_10": 0.00891,
+                "Recall_at_25": 0.01887,
+                "Recall_at_50": 0.03612,
+                "Recall_at_100": 0.06218,
+                "precision_at_1": 0.01786,
+                "precision_at_5": 0.01607,
+                "precision_at_10": 0.01786,
+                "precision_at_25": 0.015,
+                "precision_at_50": 0.01393,
+                "precision_at_100": 0.01295,
+                "mrr": 0.06386
+            },
+            {
+                "hf_subset": "sustainable_living",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.17692,
+                "ndcg_at_1": 0.12963,
+                "ndcg_at_5": 0.15201,
+                "ndcg_at_10": 0.17692,
+                "ndcg_at_25": 0.22053,
+                "ndcg_at_50": 0.25283,
+                "ndcg_at_100": 0.28336,
+                "map_at_1": 0.04498,
+                "map_at_5": 0.09571,
+                "map_at_10": 0.11353,
+                "map_at_25": 0.13095,
+                "map_at_50": 0.14093,
+                "map_at_100": 0.14616,
+                "Recall_at_1": 0.04498,
+                "Recall_at_5": 0.15885,
+                "Recall_at_10": 0.23436,
+                "Recall_at_25": 0.36151,
+                "Recall_at_50": 0.45659,
+                "Recall_at_100": 0.5814,
+                "precision_at_1": 0.12963,
+                "precision_at_5": 0.11111,
+                "precision_at_10": 0.08519,
+                "precision_at_25": 0.05852,
+                "precision_at_50": 0.04315,
+                "precision_at_100": 0.02787,
+                "mrr": 0.22846
+            },
+            {
+                "hf_subset": "aops",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.06462,
+                "ndcg_at_1": 0.07207,
+                "ndcg_at_5": 0.06166,
+                "ndcg_at_10": 0.06462,
+                "ndcg_at_25": 0.07919,
+                "ndcg_at_50": 0.08958,
+                "ndcg_at_100": 0.10645,
+                "map_at_1": 0.01341,
+                "map_at_5": 0.03251,
+                "map_at_10": 0.03859,
+                "map_at_25": 0.04331,
+                "map_at_50": 0.0456,
+                "map_at_100": 0.04742,
+                "Recall_at_1": 0.01341,
+                "Recall_at_5": 0.0488,
+                "Recall_at_10": 0.07132,
+                "Recall_at_25": 0.10913,
+                "Recall_at_50": 0.13728,
+                "Recall_at_100": 0.20174,
+                "precision_at_1": 0.07207,
+                "precision_at_5": 0.04865,
+                "precision_at_10": 0.03423,
+                "precision_at_25": 0.02054,
+                "precision_at_50": 0.01423,
+                "precision_at_100": 0.01009,
+                "mrr": 0.11634
+            },
+            {
+                "hf_subset": "theoremqa_questions",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.15072,
+                "ndcg_at_1": 0.16585,
+                "ndcg_at_5": 0.14465,
+                "ndcg_at_10": 0.15072,
+                "ndcg_at_25": 0.16037,
+                "ndcg_at_50": 0.17086,
+                "ndcg_at_100": 0.18269,
+                "map_at_1": 0.10569,
+                "map_at_5": 0.12829,
+                "map_at_10": 0.13137,
+                "map_at_25": 0.13465,
+                "map_at_50": 0.13615,
+                "map_at_100": 0.13741,
+                "Recall_at_1": 0.10569,
+                "Recall_at_5": 0.14228,
+                "Recall_at_10": 0.15447,
+                "Recall_at_25": 0.18769,
+                "Recall_at_50": 0.23606,
+                "Recall_at_100": 0.29354,
+                "precision_at_1": 0.16585,
+                "precision_at_5": 0.05171,
+                "precision_at_10": 0.03024,
+                "precision_at_25": 0.01463,
+                "precision_at_50": 0.00888,
+                "precision_at_100": 0.00571,
+                "mrr": 0.19111
+            },
+            {
+                "hf_subset": "leetcode",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.26776,
+                "ndcg_at_1": 0.28169,
+                "ndcg_at_5": 0.24429,
+                "ndcg_at_10": 0.26776,
+                "ndcg_at_25": 0.29118,
+                "ndcg_at_50": 0.30554,
+                "ndcg_at_100": 0.31939,
+                "map_at_1": 0.15293,
+                "map_at_5": 0.20698,
+                "map_at_10": 0.21776,
+                "map_at_25": 0.22438,
+                "map_at_50": 0.22678,
+                "map_at_100": 0.22818,
+                "Recall_at_1": 0.15293,
+                "Recall_at_5": 0.24871,
+                "Recall_at_10": 0.31056,
+                "Recall_at_25": 0.39507,
+                "Recall_at_50": 0.46315,
+                "Recall_at_100": 0.53697,
+                "precision_at_1": 0.28169,
+                "precision_at_5": 0.10704,
+                "precision_at_10": 0.06479,
+                "precision_at_25": 0.03099,
+                "precision_at_50": 0.01746,
+                "precision_at_100": 0.01,
+                "mrr": 0.34032
+            },
+            {
+                "hf_subset": "earth_science",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.27452,
+                "ndcg_at_1": 0.28448,
+                "ndcg_at_5": 0.25672,
+                "ndcg_at_10": 0.27452,
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+}

results/GritLM-7B/no_revision_available/BrightRetrieval.json

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+}

results/SFR-Embedding-Mistral/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/all-mpnet-base-v2/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/bge-large-en-v1.5/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+    "task_name": "BrightRetrieval"
+}

results/bm25/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/e5-mistral-7b-instruct/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/google-gecko.text-embedding-preview-0409/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+        ]
+    },
+    "task_name": "BrightRetrieval"
+}

results/gte-Qwen1.5-7B-instruct/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/gte-Qwen2-7B-instruct/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/instructor-large/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/instructor-xl/no_revision_available/BrightRetrieval.json

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+    "task_name": "BrightRetrieval"
+}

results/text-embedding-3-large/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+}

results/text-embedding-preview-0409-768/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
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+                    "eng-Latn"
+                ],
+                "main_score": 0.19503,
+                "ndcg_at_1": 0.18447,
+                "ndcg_at_5": 0.18824,
+                "ndcg_at_10": 0.19503,
+                "ndcg_at_25": 0.22567,
+                "ndcg_at_50": 0.24477,
+                "ndcg_at_100": 0.27073,
+                "map_at_1": 0.06385,
+                "map_at_5": 0.11052,
+                "map_at_10": 0.12166,
+                "map_at_25": 0.13936,
+                "map_at_50": 0.14826,
+                "map_at_100": 0.15488,
+                "Recall_at_1": 0.06385,
+                "Recall_at_5": 0.16165,
+                "Recall_at_10": 0.20624,
+                "Recall_at_25": 0.31451,
+                "Recall_at_50": 0.4032,
+                "Recall_at_100": 0.50804,
+                "precision_at_1": 0.18447,
+                "precision_at_5": 0.11456,
+                "precision_at_10": 0.08932,
+                "precision_at_25": 0.07068,
+                "precision_at_50": 0.04816,
+                "precision_at_100": 0.03369,
+                "mrr": 0.25786
+            },
+            {
+                "hf_subset": "psychology",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.27864,
+                "ndcg_at_1": 0.26733,
+                "ndcg_at_5": 0.26182,
+                "ndcg_at_10": 0.27864,
+                "ndcg_at_25": 0.30703,
+                "ndcg_at_50": 0.32835,
+                "ndcg_at_100": 0.35831,
+                "map_at_1": 0.12191,
+                "map_at_5": 0.17951,
+                "map_at_10": 0.20237,
+                "map_at_25": 0.22599,
+                "map_at_50": 0.23244,
+                "map_at_100": 0.23709,
+                "Recall_at_1": 0.12191,
+                "Recall_at_5": 0.23612,
+                "Recall_at_10": 0.31401,
+                "Recall_at_25": 0.44192,
+                "Recall_at_50": 0.52868,
+                "Recall_at_100": 0.65694,
+                "precision_at_1": 0.26733,
+                "precision_at_5": 0.1505,
+                "precision_at_10": 0.11386,
+                "precision_at_25": 0.07129,
+                "precision_at_50": 0.04337,
+                "precision_at_100": 0.02743,
+                "mrr": 0.3276
+            }
+        ]
+    },
+    "task_name": "BrightRetrieval"
+}

results/voyage-large-2-instruct/no_revision_available/BrightRetrieval.json

@@ -0,0 +1,393 @@
+{
+    "dataset_revision": "a75a0eb",
+    "mteb_version": "1.12.79",
+    "scores": {
+        "standard": [
+            {
+                "hf_subset": "theoremqa_questions",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.26057,
+                "ndcg_at_1": 0.26829,
+                "ndcg_at_5": 0.24914,
+                "ndcg_at_10": 0.26057,
+                "ndcg_at_25": 0.27232,
+                "ndcg_at_50": 0.28147,
+                "ndcg_at_100": 0.2865,
+                "map_at_1": 0.15935,
+                "map_at_5": 0.2211,
+                "map_at_10": 0.2282,
+                "map_at_25": 0.23207,
+                "map_at_50": 0.23351,
+                "map_at_100": 0.23411,
+                "Recall_at_1": 0.15935,
+                "Recall_at_5": 0.25894,
+                "Recall_at_10": 0.28525,
+                "Recall_at_25": 0.32265,
+                "Recall_at_50": 0.3637,
+                "Recall_at_100": 0.3885,
+                "precision_at_1": 0.26829,
+                "precision_at_5": 0.09756,
+                "precision_at_10": 0.05561,
+                "precision_at_25": 0.02576,
+                "precision_at_50": 0.01444,
+                "precision_at_100": 0.00776,
+                "mrr": 0.30628
+            },
+            {
+                "hf_subset": "earth_science",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.25086,
+                "ndcg_at_1": 0.19828,
+                "ndcg_at_5": 0.22776,
+                "ndcg_at_10": 0.25086,
+                "ndcg_at_25": 0.28635,
+                "ndcg_at_50": 0.31158,
+                "ndcg_at_100": 0.33522,
+                "map_at_1": 0.07348,
+                "map_at_5": 0.17055,
+                "map_at_10": 0.18716,
+                "map_at_25": 0.19891,
+                "map_at_50": 0.20524,
+                "map_at_100": 0.20885,
+                "Recall_at_1": 0.07348,
+                "Recall_at_5": 0.24151,
+                "Recall_at_10": 0.3118,
+                "Recall_at_25": 0.41213,
+                "Recall_at_50": 0.49555,
+                "Recall_at_100": 0.58017,
+                "precision_at_1": 0.19828,
+                "precision_at_5": 0.1431,
+                "precision_at_10": 0.09914,
+                "precision_at_25": 0.05828,
+                "precision_at_50": 0.03707,
+                "precision_at_100": 0.02388,
+                "mrr": 0.30694
+            },
+            {
+                "hf_subset": "leetcode",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.30601,
+                "ndcg_at_1": 0.24648,
+                "ndcg_at_5": 0.26509,
+                "ndcg_at_10": 0.30601,
+                "ndcg_at_25": 0.33494,
+                "ndcg_at_50": 0.35717,
+                "ndcg_at_100": 0.37809,
+                "map_at_1": 0.13979,
+                "map_at_5": 0.21046,
+                "map_at_10": 0.23036,
+                "map_at_25": 0.24003,
+                "map_at_50": 0.24445,
+                "map_at_100": 0.2469,
+                "Recall_at_1": 0.13979,
+                "Recall_at_5": 0.30962,
+                "Recall_at_10": 0.41514,
+                "Recall_at_25": 0.51467,
+                "Recall_at_50": 0.60986,
+                "Recall_at_100": 0.72183,
+                "precision_at_1": 0.24648,
+                "precision_at_5": 0.10986,
+                "precision_at_10": 0.07535,
+                "precision_at_25": 0.03775,
+                "precision_at_50": 0.02268,
+                "precision_at_100": 0.01331,
+                "mrr": 0.35788
+            },
+            {
+                "hf_subset": "economics",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.19848,
+                "ndcg_at_1": 0.16505,
+                "ndcg_at_5": 0.18293,
+                "ndcg_at_10": 0.19848,
+                "ndcg_at_25": 0.23365,
+                "ndcg_at_50": 0.2535,
+                "ndcg_at_100": 0.2795,
+                "map_at_1": 0.07683,
+                "map_at_5": 0.10967,
+                "map_at_10": 0.1278,
+                "map_at_25": 0.14619,
+                "map_at_50": 0.1568,
+                "map_at_100": 0.16389,
+                "Recall_at_1": 0.07683,
+                "Recall_at_5": 0.1431,
+                "Recall_at_10": 0.21009,
+                "Recall_at_25": 0.35848,
+                "Recall_at_50": 0.43738,
+                "Recall_at_100": 0.53645,
+                "precision_at_1": 0.16505,
+                "precision_at_5": 0.11456,
+                "precision_at_10": 0.09612,
+                "precision_at_25": 0.06951,
+                "precision_at_50": 0.05087,
+                "precision_at_100": 0.03447,
+                "mrr": 0.2417
+            },
+            {
+                "hf_subset": "robotics",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.11214,
+                "ndcg_at_1": 0.06931,
+                "ndcg_at_5": 0.10135,
+                "ndcg_at_10": 0.11214,
+                "ndcg_at_25": 0.1392,
+                "ndcg_at_50": 0.16071,
+                "ndcg_at_100": 0.18233,
+                "map_at_1": 0.00995,
+                "map_at_5": 0.05639,
+                "map_at_10": 0.06438,
+                "map_at_25": 0.07521,
+                "map_at_50": 0.0803,
+                "map_at_100": 0.08355,
+                "Recall_at_1": 0.00995,
+                "Recall_at_5": 0.11045,
+                "Recall_at_10": 0.14971,
+                "Recall_at_25": 0.25345,
+                "Recall_at_50": 0.3261,
+                "Recall_at_100": 0.41417,
+                "precision_at_1": 0.06931,
+                "precision_at_5": 0.07327,
+                "precision_at_10": 0.05644,
+                "precision_at_25": 0.03881,
+                "precision_at_50": 0.02733,
+                "precision_at_100": 0.01851,
+                "mrr": 0.14479
+            },
+            {
+                "hf_subset": "psychology",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.24793,
+                "ndcg_at_1": 0.18812,
+                "ndcg_at_5": 0.22226,
+                "ndcg_at_10": 0.24793,
+                "ndcg_at_25": 0.27639,
+                "ndcg_at_50": 0.30524,
+                "ndcg_at_100": 0.33332,
+                "map_at_1": 0.08328,
+                "map_at_5": 0.14382,
+                "map_at_10": 0.17498,
+                "map_at_25": 0.19453,
+                "map_at_50": 0.20294,
+                "map_at_100": 0.20784,
+                "Recall_at_1": 0.08328,
+                "Recall_at_5": 0.19782,
+                "Recall_at_10": 0.31014,
+                "Recall_at_25": 0.42584,
+                "Recall_at_50": 0.534,
+                "Recall_at_100": 0.65502,
+                "precision_at_1": 0.18812,
+                "precision_at_5": 0.14257,
+                "precision_at_10": 0.1099,
+                "precision_at_25": 0.0701,
+                "precision_at_50": 0.04792,
+                "precision_at_100": 0.03059,
+                "mrr": 0.27718
+            },
+            {
+                "hf_subset": "aops",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.07449,
+                "ndcg_at_1": 0.07207,
+                "ndcg_at_5": 0.06813,
+                "ndcg_at_10": 0.07449,
+                "ndcg_at_25": 0.08626,
+                "ndcg_at_50": 0.10085,
+                "ndcg_at_100": 0.11047,
+                "map_at_1": 0.01051,
+                "map_at_5": 0.03632,
+                "map_at_10": 0.04796,
+                "map_at_25": 0.05131,
+                "map_at_50": 0.05369,
+                "map_at_100": 0.0547,
+                "Recall_at_1": 0.01051,
+                "Recall_at_5": 0.05454,
+                "Recall_at_10": 0.08655,
+                "Recall_at_25": 0.11803,
+                "Recall_at_50": 0.16693,
+                "Recall_at_100": 0.20383,
+                "precision_at_1": 0.07207,
+                "precision_at_5": 0.06486,
+                "precision_at_10": 0.05045,
+                "precision_at_25": 0.02595,
+                "precision_at_50": 0.01712,
+                "precision_at_100": 0.01027,
+                "mrr": 0.11313
+            },
+            {
+                "hf_subset": "sustainable_living",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.1558,
+                "ndcg_at_1": 0.12037,
+                "ndcg_at_5": 0.12891,
+                "ndcg_at_10": 0.1558,
+                "ndcg_at_25": 0.19509,
+                "ndcg_at_50": 0.22142,
+                "ndcg_at_100": 0.25656,
+                "map_at_1": 0.04147,
+                "map_at_5": 0.08338,
+                "map_at_10": 0.10179,
+                "map_at_25": 0.11829,
+                "map_at_50": 0.12454,
+                "map_at_100": 0.13091,
+                "Recall_at_1": 0.04147,
+                "Recall_at_5": 0.12661,
+                "Recall_at_10": 0.20543,
+                "Recall_at_25": 0.32307,
+                "Recall_at_50": 0.40798,
+                "Recall_at_100": 0.5478,
+                "precision_at_1": 0.12037,
+                "precision_at_5": 0.08704,
+                "precision_at_10": 0.075,
+                "precision_at_25": 0.05074,
+                "precision_at_50": 0.03611,
+                "precision_at_100": 0.02593,
+                "mrr": 0.20076
+            },
+            {
+                "hf_subset": "pony",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.01481,
+                "ndcg_at_1": 0.02679,
+                "ndcg_at_5": 0.02011,
+                "ndcg_at_10": 0.01481,
+                "ndcg_at_25": 0.01324,
+                "ndcg_at_50": 0.01723,
+                "ndcg_at_100": 0.02295,
+                "map_at_1": 0.00135,
+                "map_at_5": 0.0032,
+                "map_at_10": 0.00331,
+                "map_at_25": 0.00362,
+                "map_at_50": 0.00398,
+                "map_at_100": 0.00432,
+                "Recall_at_1": 0.00135,
+                "Recall_at_5": 0.00507,
+                "Recall_at_10": 0.0058,
+                "Recall_at_25": 0.01043,
+                "Recall_at_50": 0.01873,
+                "Recall_at_100": 0.03085,
+                "precision_at_1": 0.02679,
+                "precision_at_5": 0.01786,
+                "precision_at_10": 0.01071,
+                "precision_at_25": 0.00857,
+                "precision_at_50": 0.00768,
+                "precision_at_100": 0.00652,
+                "mrr": 0.05551
+            },
+            {
+                "hf_subset": "theoremqa_theorems",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.10128,
+                "ndcg_at_1": 0.09231,
+                "ndcg_at_5": 0.09778,
+                "ndcg_at_10": 0.10128,
+                "ndcg_at_25": 0.13144,
+                "ndcg_at_50": 0.15753,
+                "ndcg_at_100": 0.18175,
+                "map_at_1": 0.03385,
+                "map_at_5": 0.0741,
+                "map_at_10": 0.07716,
+                "map_at_25": 0.08486,
+                "map_at_50": 0.08921,
+                "map_at_100": 0.09167,
+                "Recall_at_1": 0.03385,
+                "Recall_at_5": 0.12103,
+                "Recall_at_10": 0.12872,
+                "Recall_at_25": 0.24154,
+                "Recall_at_50": 0.36077,
+                "Recall_at_100": 0.48872,
+                "precision_at_1": 0.09231,
+                "precision_at_5": 0.04923,
+                "precision_at_10": 0.02769,
+                "precision_at_25": 0.01785,
+                "precision_at_50": 0.01262,
+                "precision_at_100": 0.00862,
+                "mrr": 0.13921
+            },
+            {
+                "hf_subset": "biology",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.23552,
+                "ndcg_at_1": 0.18447,
+                "ndcg_at_5": 0.20546,
+                "ndcg_at_10": 0.23552,
+                "ndcg_at_25": 0.27602,
+                "ndcg_at_50": 0.29962,
+                "ndcg_at_100": 0.32645,
+                "map_at_1": 0.05303,
+                "map_at_5": 0.13537,
+                "map_at_10": 0.16143,
+                "map_at_25": 0.17948,
+                "map_at_50": 0.18628,
+                "map_at_100": 0.19024,
+                "Recall_at_1": 0.05303,
+                "Recall_at_5": 0.20556,
+                "Recall_at_10": 0.29298,
+                "Recall_at_25": 0.41167,
+                "Recall_at_50": 0.49,
+                "Recall_at_100": 0.60618,
+                "precision_at_1": 0.18447,
+                "precision_at_5": 0.14951,
+                "precision_at_10": 0.10971,
+                "precision_at_25": 0.06214,
+                "precision_at_50": 0.03786,
+                "precision_at_100": 0.02272,
+                "mrr": 0.30076
+            },
+            {
+                "hf_subset": "stackoverflow",
+                "languages": [
+                    "eng-Latn"
+                ],
+                "main_score": 0.15034,
+                "ndcg_at_1": 0.12821,
+                "ndcg_at_5": 0.13332,
+                "ndcg_at_10": 0.15034,
+                "ndcg_at_25": 0.19195,
+                "ndcg_at_50": 0.2278,
+                "ndcg_at_100": 0.24506,
+                "map_at_1": 0.0434,
+                "map_at_5": 0.08455,
+                "map_at_10": 0.09794,
+                "map_at_25": 0.11497,
+                "map_at_50": 0.12426,
+                "map_at_100": 0.12738,
+                "Recall_at_1": 0.0434,
+                "Recall_at_5": 0.11759,
+                "Recall_at_10": 0.17524,
+                "Recall_at_25": 0.29522,
+                "Recall_at_50": 0.41908,
+                "Recall_at_100": 0.47921,
+                "precision_at_1": 0.12821,
+                "precision_at_5": 0.09744,
+                "precision_at_10": 0.0735,
+                "precision_at_25": 0.05538,
+                "precision_at_50": 0.04137,
+                "precision_at_100": 0.02538,
+                "mrr": 0.2073
+            }
+        ]
+    },
+    "task_name": "BrightRetrieval"
+}

scripts/convert_bright.py

@@ -0,0 +1,64 @@
+import json
+import os
+
+REPLACE_MAP = {
+    "NDCG": 'ndcg',
+    "MAP": 'map',
+    "MRR": 'mrr',
+    "RECALL": 'recall',
+    "P": 'precision',
+}
+
+MODEL_TO_MODEL = {
+    "bm25": "bm25",
+    "bge": "bge-large-en-v1.5",
+    "cohere": "Cohere-embed-english-v3.0",
+    "e5": "e5-mistral-7b-instruct",
+    "google": "google-gecko.text-embedding-preview-0409",
+    "grit": "GritLM-7B",
+    "inst-l": "instructor-large",
+    "inst-xl": "instructor-xl",
+    "openai": "text-embedding-3-large",
+    "qwen2": "gte-Qwen2-7B-instruct",
+    "qwen": "gte-Qwen1.5-7B-instruct",
+    "sbert": "all-mpnet-base-v2",
+    "sf": "SFR-Embedding-Mistral",
+    "voyage": "voyage-large-2-instruct",
+}
+folders = os.listdir('bright_scores')
+print(folders)
+models = set([x.split("_")[-3] for x in folders if os.path.isdir('bright_scores/' + x)])
+for model in models:
+    print(f"Converting {model}")
+    result_template = {
+        "dataset_revision": "a75a0eb",
+        "mteb_version": "1.12.79",
+        "scores": {
+            "standard": []
+        },
+        "task_name": "BrightRetrieval",
+    }
+    for folder in [x for x in folders if (os.path.isdir('bright_scores/' + x)) and (x.split("_")[-3] == model)]:
+        results_path = 'bright_scores/' + folder + '/results.json'
+        if len(folder.split("_")) == 4:
+            split = folder.split("_")[0]
+        elif len(folder.split("_")) == 5:
+            split = folder.split("_")[0] + "_" + folder.split("_")[1]
+        with open(results_path) as f:
+            results = json.load(f)
+        result_template['scores']['standard'].append(
+            {
+                "hf_subset": split,
+                "languages": ["eng-Latn"],
+                "main_score": results["NDCG@10"],
+                **{"_at_".join([REPLACE_MAP.get(x, x) for x in k.split("@")]): v for k,v in results.items()}
+            }
+        )
+    
+    model_folder = MODEL_TO_MODEL[model]
+    os.makedirs(f"results/{model_folder}/no_revision_available", exist_ok=True)
+    print(f"Writing to: results/{model_folder}/no_revision_available/BrightRetrieval.json")
+    with open(f"results/{model_folder}/no_revision_available/BrightRetrieval.json", "w") as f:
+        json.dump(result_template, f, indent=4)
+
+